Computation of radiating particle and wave distributions using a generalized discrete field constructed from representative ray sets

ABSTRACT

The present system and method for simulating particles and waves is useful for calculations involving nuclear and full spectrum radiation transport, quantum particle transport, plasma transport and charged particle transport. The invention provides a mechanism for creating accurate invariants for embedding in general three-dimensional problems and describes means by which a series of simple single collision interaction finite elements can be extended to formulate a complex multi-collision finite element.

REFERENCE TO RELATED APPLICATIONS

This application is a Divisional of U.S. patent application Ser. No.10/790,404 filed Mar. 1, 2004, now U.S. Pat. No. 7,197,404.

INTRODUCTION

The present teachings relate to a process for simulating thetransportation of particles and/or waves propagating through a medium.The present teachings further relate to the process of utilizingcomputational algorithms and methodologies to simulate both nuclear andelectromagnetic radiation transmitting and interacting within aparticular medium. The present teachings have many differentapplications and address numerous problems in the prior art.

For over a century, physicians have been administering ionizingradiation to patients for the purpose of treating various types ofcancerous tumors. Throughout this time, many advances have been madewithin the Radiation Oncology field. One particular technologicaladvance has been the practice of modulating the intensity of theradiation prior to exposing the patient. Upon modulating the radiation,the beam becomes non-uniform and the dose distribution profile isirregular. The irregular dose distribution is advantageous in that, themaximum dose or intensity of the beam can be precisely positioned withinand confined to the target volume of the tumor while simultaneouslyminimizing the dose or intensity of the beam to healthy tissuesurrounding the tumor.

Presently, such practice is often referred to as Intensity ModulatedRadiation Therapy, hereinafter referred to as IMRT. Modulating theintensity of the beam is a conventional practice and has been used inthe field of Radiation Oncology for decades. More recently, the useMultileaf Collimators (MLC) to modulate beam intensities has becomepopular since MLC have the dual capability of performing ThreeDimensional Conformal Radiation Therapy (3DCRT) and IMRT.

However, due to the complexity of intensity modulated beams and/or theirirregular field shapes produced by the MLC, determining depth dosedistributions within the patients is extremely difficult, if notimpossible through manual calculations. As such, the employment ofcomputational algorithms is required. Through the use of computers,ionizing radiation propagating through and interacting with a materialcan be simulated through algorithms. Therefore, such computationalalgorithms can provide the necessary depth dose distributions withoutlaborious manual calculations.

For many years, Monte Carlo or stochastic methods have been used todetermine particle transport in three dimensions. Such computationalmethodologies for particle and wave simulations are used in a number ofapplications. For instance, in Intensity Modulated Radiation Therapythree-dimensional treatment planning, Monte Carlo calculations areemployed, as well as semi-empirical methods based on a conventionalray—buildup factor technique. Additionally, fast Fourier transforms arerecurrently used with superposition to compute scattered dose throughoutthe tissue phantom. In the nuclear engineering field, α, β and γ neutronand proton radiation is simulated in nuclear transport calculations forapplications ranging from nuclear reactor core design to shielding tonuclear medicine. In addition, numerous equipment designs, ranging fromradiation detectors to microwave ovens, also profit from transportcomputations.

Radiative heat transfer, useful for combustor and boiler design, isanother area in which computational methods have been used. Forinstance, in the “discrete transfer method” representative rays betweenbounded reflecting surfaces are used to model thermal radiationtraversing a grid system of control volumes. Diverse integration kernelsare applied to specific lengths associated with these rays to determinethe intensity of beams passing through the system. The intensity ofrepresentative rays is computed on entrance and exit to control volumeswith rays reflecting off of fixed problem boundaries.

The “Invariant Imbedding” technique involves imbedding invariantsolutions within a large framework and coupling finite element solutionsand nodal computations. This technique, however, retains functionalparticle density continuity concepts that are relatively inaccurate forextension beyond one-dimension (although using the discrete transfermethod to determine finite element imbedding invariants provides apractical means of extending ray solutions to far larger problems).

While the existing methods are adequate for many computationalapplications, they possess a number of shortcomings. One significantshortcoming is their failure to provide mechanisms for reducing anextremely large set of rays to simple scalar multipliers that can beused to compute radiation distributions from distributed sources in ageneralized field. Another disadvantage is that the known methodologiesdo not describe how a local system of representative rays can beextended from one set of control volumes to another set. The methods ofthe prior art also fail to include mechanisms for creating invariantvoxel groups that can be imbedded in larger systems using a generalizedfield. A further shortcoming of the existing methodologies is that theyfail to provide means by which a series of simple single collisioninteraction finite elements can be extended to formulate a complexmulti-collision finite element. Finally, the methods of the prior artlack a generalized scheme for all forms of radiating particles (rangingfrom electromagnetic radiation to quantum particles and from neutrons tosound waves). Accordingly, there is a need for a computationalmethodology that overcomes the shortcomings of the prior art.

Thus, on account of the foregoing shortcomings, while prior artcomputational methods are capable of providing solutions to transportsimulations, they are unable to simulate particle transport with bothacceptable speed and accuracy. Typical methods such as Monte Carlo, can,under certain conditions, produce a simulation with sufficient accuracy,but they fail to perform the simulation within a reasonable amount oftime to be used within a Radiation Oncology clinic and can be tooexotic, expensive and time consuming for other applications, such aselectronic device design. Alternatively, other simulations possess theneeded speed, but in doing so sacrifice essential accuracy. As such,there is a critical need in the art to provide a radiation transportsimulation computer methodology capable of achieving acceptablesimulation speed and accuracy.

SUMMARY

The present teachings relate to systems and computational methodologiesfor simulating particles and waves. These teachings are useful forcalculations involving nuclear and full spectrum (radio to gamma ray)electromagnetic radiation transport as well as quantum particletransport, plasma transport and charged particle transport. Theseteachings are also useful for vibrational/sound wave computations andradiative heat transfer.

The present teachings represent a significant conceptual departure fromthe prior art in that functional representations of particle densitieshave been dropped in favor of a discrete particle value within adiscrete finite element solved using direct methods. The presentteachings advantageously provide a mechanism for creating accurateinvariants [See R. Belman, G. M. Wing, An Introduction to InvariantImbedding, R. Belman, SIAM (1992) ISBN 0-89871-304-8] for embedding ingeneral three-dimensional (“3D”) problems and describes means by which aseries of simple single collision interaction finite elements can beextended to formulate a complex multi-collision finite element.

Results show that the teachings described herein are at least ten (10),and as much as one thousand (1000) times or more faster than thecomputational methods of the prior art. Results also show significantimprovement over direct method, ray effect intensive mitigationbenchmark problems. The foregoing results were obtained using a digitalcomputer. However, the present teachings also contemplate the use ofanalogue and digital-analogue hybrids for specialty—fast computation ofparticle transport. For instance, the teachings can be used with a fastanalogue control system in Intensity Modulated Radiation Therapythree-dimensional treatment planning to provide real-time computation ofparticle transport for use on external beam machines in conjunction withpatient position data.

As stated above, the examples provided herein are primarily described interms of a computational device for calculating radiation distributionfor medical applications. Specifically referenced are examples regardingIntensity Modulated Radiation Therapy three-dimensional treatmentplanning (hereinafter “IMRT 3DRTP”) for computation of gamma and x-rayparticle transport in human tissue used for cancer radiotherapy.However, the examples also demonstrate the application of the inventionin the other fields. These fields include, but are not limited to, thefollowing: medical radiation treatment planning simulation, electronicdevice design (e.g. microwave ovens and computer chips), RADAR (forelectromagnetic radiation modeling), SONAR (for sound wave simulation),electrical transmission device design, optics, radiative heat transfer,nuclear shielding and reactor simulation, astrophysics, boiler andcombustion simulation, telecommunications (e.g. to determine the optimalplacement of cell phone towers), and mechanics.

The adaptability of the present teachings are due, in part, to theirunique algorithmic machinery, which is capable of modeling variousapplications through the use of replaceable (referring to a functionpointer in C, or an object oriented computer language) integrationkernels and interaction models. As defined below, Integration Kernelsare used within FIG. 10 B.5, FIG. 11 B.3 and FIG. 12 B.3. An InteractionModel is explicitly shown in FIG. 7 Block 5. These and other features ofthe present teachings are set forth herein.

DEFINITIONS

The following terms are prescribed definitions to aid and facilitate inthe understanding of the present teachings and such terms are notintended to be repugnant to their conventional meanings.

Particle—A particle is defined as a packet of energy being of dualnature, in that the packet of energy can be a discrete finite object ora wave having a particular wavelength or frequency. This concept of thedual state of energy is not novel, but has been conventionally acceptedwithin the general scientific field.

Examples of particles conforming to this definition include but are notlimited to electromagnetic radiation such as γ-rays, x-rays, as well asother nuclear particles such as neutrons, alpha particles, protons andelectrons. Δx—Bold Δx represents a finite interval from x to x+Δx of thevariable x.

In reference to surfaces, Δs represents a discrete surface plane offinite area (not the linear measure associated with an arc). Note thatthis bold notation for Δx refers to the present teachings, while theun-highlighted Δx refers to the classical mathematic increment.

Discrete Particle—As a key component of the instant teachings, adiscrete particle is a real number tally in which a number of particlesare represented within discrete finite state variables (i.e., a discretephase space).

Within IMRT 3DRTP, a discrete particle is defined as P[Δs, ΔΩ, Δt],wherein P is the number of particles associated with a surface plane offinite area Δs, traversing within solid angle set ΔΩ and within finiteinterval of time Δt. However as a mathematical device it is not at apoint, but rather a constant value across a finite interval.

Although the foregoing discrete particle state variables Δs, ΔΩ and Δt,are the preferred state variables for simulating Radiation Transport forIMRT 3DRTP, the instant teachings are not limited to only thesevariables. Various other state variables include, but are not limitedto, discrete energy groups, charge, quantum spin, or ray sets (definedbelow).

Voxel—A voxel can be thought of as the volume form of a pixel wherein apixel is pertinent to an image within a two dimensional plane, a voxelis associated with a three dimensional image of an object. Each voxelrepresents both the spatial surfaces and the bounded homogenizedmaterial composition disposed therein. Although it is less difficult tovisualize a regular shape as a voxel, such as a cube with six (6)surface sides, voxels need not be of uniform shape. As an example, in3DRTP IMRT, Voxels may be shaped as parts of bones and may encompass oneor several sections of an organ. The term voxel is common to prior artdescriptions of IMRT computer solutions.

Grid—A grid, in the instant teachings, is considered to be a finitesystem of adjacent voxels with a number of surfaces on each voxel. Asimple conceptual example would be a 3³ set of voxels forming a cube,wherein each voxel has six surface faces. A grid system is used torepresent a physical system of material in three-dimensional space. Itshould be emphasized that voxels and grids may be regular or irregularin shape. For instance, in 3DRTP IMRT an irregular grid system of voxelsis shaped to match critical regions including, but not limited to,organs and bones.

Ray Set—A ray set is a number of representative rays from 1 to n, wheren is a finite integer. These rays represent the average relativefraction and voxel path length of all rays emanating from a surface orvolume, and traversing a grid. Each ray within a ray set has a uniquefraction and unique voxel path length. However the ray traverses thesame pathway through the voxel grid as other members of its set.

A ray set is distinguishable from a beamlet in that a ray set representsthe geometric pathways over which radiation traverses while a beamlet isan actual beam of radiation emanating from a source with a specificenergy spectrum. Ray sets are used throughout the phantom/problem mediato couple all locations within a phantom/problem, whereas beamlets aretypically external to a phantom and project towards a phantom from afixed point.

Local Voxel Group—A Local Voxel Group (LVG) is a subset of grids orcollection or grouping of voxels referenced from a particular voxelsurface or volume. From this voxel surface or volume, ray sets emanatethrough local voxels and extend to the outer surface of the local voxelgroup. The referenced voxel surface is important because local voxelgroups overlap and are not fixed finite elements except in reference toa particular surface.

Interaction Model—Interaction Model (IM) refers to the methodologyapplied in computing the distribution of particles once they are talliedas colliding with intervening material within voxels. An InteractionModel is required to model particle interaction, reflection or scatterand state change. Interaction Modules determine discrete particledistribution emanating from a voxel surface by considering all relevantmaterial and state variables. Interaction Modules also determineparticle distributions for ray sets within discrete solid angle groupsΔΩ.

Interaction Tallies—Closely related to the concept of an InteractionModel is the concept of an interaction tally. The interaction tally maybe as simple as a singular homogenous count of interactions that occurswithin voxel volumes, to a sophisticated tally that embodies particleentrance state values such as Δs, ΔE, ΔΩ, Δt and ΔR or other statevariables including, but not limited to quantum spin (quantummechanics), force (mechanical applications) or temperature (radiativeheat transfer). Interaction tallies may also apply to functioncoefficients. For example, the present teachings may be used to directlypopulate function coefficients associated with surface or sphericalharmonic representation of angular particle distributions.

Homogenized—This term refers to volumetric averaged or interactionaveraged material properties used in particle transport calculations.

Integration Kernels—For particle transport, integration kernels refer tothe straight-line un-collided intensity kernel associated withparticles. For most forms of radiating particles, the appropriateintegration kernel is simply e^(−ΣΔr), where Σ is the total macroscopiccross-section for particle-material interaction and Δr is the distancetraversed within a particular voxel. This represents the un-collidedtransmitted particle, while 1−e^(−ΣΔr) represents the interactingcomponent. In instances where the interaction model assumes modifiedP_(l) isotropic scatter, Σ represents the transport cross section asopposed to the total cross section.

Transport Multipliers—This term refers to a numerical value as describedin FIGS. 6 and 10 representing the final summation of the uncollidedfraction of discrete particles transported from a source location(surface or volume) to a terminal location (surface, volume, or functioncoefficient).

Terminal—Terminal refers to a position or function coefficient where aparticle or fraction of attenuated particles end are accumulated. Therecan be multiple terminals per voxel and surface. A pointer may point toa terminal multiplier or a plurality of multipliers associated withseveral terminals.

Terminal Surface—A Terminal Surface is the surface bounding a LocalVoxel Group upon which particles are tallied.

Substantial Convergence—A value that sufficiently approaches themathematically idealized convergence value.

BRIEF DESCRIPTION OF THE DRAWINGS

The skilled artisan will understand that the drawings, described below,are for illustration purposes only. The drawings are not intended tolimit the scope of the present teachings in any way.

FIG. 1 illustrates a simple representation of a real single particlepassing through a surface plane of finite area Δs.

FIG. 2 is an illustrative example of the grid of the present teachingswhich is compiled from a 3³ arrangement of voxels having bentrectangular regions.

FIG. 3 depicts a subset of rays traversing a two dimensional grid systemfrom a reference surface.

FIG. 4 represents a group of rays emanating from a surface plane offinite area and traversing a plurality of voxels wherein the group ofrays occupy a solid angle group ΔΩ within a particular ray set.

FIG. 5 depicts a reference surface within a Local Voxel Group (LVG) andcontained within the grid system.

FIG. 6 depicts a data representation of the tally memory device used tostore transport multipliers and pointers.

FIG. 7 is representative of embodiments of the present teachings withlogical process pathways and alternative paths in a block body diagramformat. Each block within the figure represents a step or a computersoftware directive in the process of simulating the transport ofparticles through a medium.

FIG. 8 depicts a block body diagram setting forth the logical stepsperformed to computationally simulate ray sets of the present teachings.

FIG. 9 represents a simulated output for a particular ray set obtainedby utilizing the block body diagram steps set forth in FIG. 8 of thepresent teachings.

FIG. 10 illustrates an additional block body diagram sequentiallydepicting the steps required to determine LVG multipliers of the presentteachings.

FIG. 11 Inline Ray Set Based LVG Discrete Particle TransportMultipliers, FIG. 7 Block 2 Embodiment. FIG. 11 depicts processes and/orobject blocks.

FIG. 12 through a simple flow diagram, describes the steps performed insimulating a particle within an Interactive Model.

FIG. 13 depicts a sample problem layout for FIG. 14.

FIG. 14 depicts simulated radiation transport outputs for both theinstant invention and a prior art technique. Although the accuracy ofboth systems is comparable, the instant invention computes thesimulation nearly 1000 times faster.

FIG. 15 presents simulated radiation transport result comparisons forproblem 1A of an international standard benchmark set “3D RadiationTransport Benchmark Problems and Results for Simple Geometries with VoidRegion”, ISSN 0149-1970 “Progress in Nuclear Energy”. The resultscompare the present teachings with other respected computer codesshowing the accuracy improvement of the present teachings.

FIG. 16 shows a comparison of problem 1A results with a cut surfaceillustrating the present teachings' ability to accurately predictresults behind a tally deposition surface. This illustrates the abilityof the present teachings to simulate embedded problems.

FIG. 17 presents a time and machine comparison for solution of problems1A results as shown in FIGS. 15 and 16. These time comparisonsillustrate the 1000 fold speed advantage of the instant teachings.

DESCRIPTION OF VARIOUS EMBODIMENTS

Monte Carlo tracks each discrete particle history exactly and develops astochastic result using hundreds of millions (if not billions) of exactparticle histories (E. Cashwell & C. Everett, The Practical Manual onthe Monte Carlo Method for Random Walk Problems, Pergamon Press (1959)).The present teachings invert this process by defining discrete particlesthat occupy computer memory in a detailed phase space—essentiallyrepresenting millions of distinct phase space particle count values.Stated otherwise, the present teachings exactly and efficiently computesdistributed phase space discrete particle transport to local surfaces,function coefficients and volumes, reducing the results of thesecalculations to a multiplication field appropriate for each surface,function coefficient and volume. The approximation involved in thecalculation is the assumption that the increment of the discreteparticle itself is truly constant. Thereafter, “exact” calculations areused to determine generalized field transport multipliers in a localarea to create continuity, with extensions to a generalized area.

Discrete particles emanating from surfaces and volumes are directly“wired” to LVG neighboring surfaces, function coefficients and volumesthrough multiplier, voxel pointer pairs—to provide a near exact localsolution of particle transport (the assumption being the constancy ofthe discrete particle itself). The LVG provides a local exact solutionthat reduces the particle count contribution from a local referencevoxel volume or surface to external voxel volumes and surfaces. Thisprovides the accuracy required to tackle three-dimensional problems, asopposed to imbedded invariants methods that break down past onedimension. The LVG multiplier field greatly reduces ray effects asparticles are properly distributed in a local system. Those particlesthat are not distributed within the LVG are attenuated and emanate fromthe LVG surface. The distribution of particles emanating from a surfacemay be explicitly tracked either through a direct tally process orfunction deposition in a least squares sense to determine the angulardistribution at the surface interface. In a similar fashion, functioncoefficient tallies may be used with complex interaction models to allowfor high order particle scattering, for example anisotropic P₅.

A ray set concept is introduced to the phase space of the discreteparticles to provide accurate LVG-to-LVG surface interface transport ofparticles, further improving accuracy. The computer memory system isthen swept, allowing discrete particles to conceptually travel fromsurfaces to surfaces, from surfaces to volumes, from volumes to surfacesand from volumes to volumes in an abstract sense using the transportmultiplier/pointer system (FIG. 7 Block 4). Any number of possibleinteraction models (for example, simple nuclear particle mono-energeticisotropic scatter, or multigroup, anisotropic scatter) are then employedto adjust particle interaction within volumes and make appropriate phasespace adjustment to continue particle transport sweeps (FIG. 7 Blocks 4,5 and 6). The instant teachings can be used to create an appropriateinteraction model.

By decoupling interaction from multiple collision transport, exactdirect local analytic solutions along ray paths through voxels arepossible. The Interaction Model then serves to produce new voxeldiscrete particle sources for further discrete particle transportsweeps. Thus by employing exact transport solutions of approximatediscrete particles, high accuracy is achieved through the use of manyphase space particles. Single multipliers are employed within the LVG,providing direct non-stochastic results very quickly.

The fundamental difference between this method and a classic Green'sFunction Approach [R. D. Lawrence and J. J. Dorning, A Nodal Green'sFunction Method for Multidimensional Diffusion Calculations, NuclearScience and Engineering 76, 218-231 (1980)] is that a Green's functionsolves a boundary value problem either over the entire time domain andall scattering interactions moments, and is therefore constrained toboundary conditions. Whether one solves a 1-D or multidimensionalGreen's function response, the Green's function describes all eventsthat occur between two points over time. In the present teachings, theone-dimensional first flight collision solution is a transport solutionwith a vacuum boundary that is irrelevant in terms of time. As a result,there is no approximation; it is a true transport solution. An outeriteration and separate interaction model account for scatteringinteractions and reaction rate/transient variables. The Green's functionapproach creates a full matrix response that includes scatter and, as aresult, approximations such as modified diffusion theory must be made.What the present teachings and Green's functions share is locationcoupling. However, the present teachings provide far greater accuracy byseparating out the scatter component in a separate time and scatteringiteration.

The use of discrete particle definitions completely differentiates thepresent teachings from all prior art. Furthermore, the reduction of rayset data to form memory pointer/multiplier pairs is also unique to theinstant teachings, as is the use of ray sets ΔR to provide extensionsfor accurate LVG-to-LVG particle transport.

Explanation of the Drawings The figures contained herein sequentiallydescribe the invention and the manner in which it may be utilized toovercome the problems in the prior art. Set forth below is a descriptionand explanation of each figure.

FIG. 1

This figure presents a simple depiction of a finite incremental surfacetraversed by a representative particle on a particular ray. Ray tracingfrom volumes to surfaces may deposit particles uniformly on the smallestincremental surface. Rays emanating from surfaces may be assumed toproceed from the surface center, losing some information. Alternatively,in a pre-compute variant of the instant teachings, surface distributionof rays may be uniform across the surface. The angular representation ofparticles across rays may be explicit for all angles for each incomingray set. Alternatively, particles from volumes to surfaces may bedeposited to function coefficients constructed from a least squareserror matrix to provide for surface data compression (See FIG. 6description).

In some embodiments of the present teachings, function coefficients canbe used to determine detailed spatial particle distributions across asingle surface, sub-surface or plurality of surfaces. These methods arediscussed in greater detail in the FIG. 6 description.

FIG. 2

This figure graphically illustrates the voxel and grid system concept.Voxels may be regular or irregular in shape, as depicted. Voxels may beof a homogenous material, or they may be bounded surfaces withheterogeneous sub-voxels within a larger system of grids. In the lattercase, the voxel response is specified by the surface interface. Aheterogeneous voxel can be generated by the present teachings, andembedded into a grid system. In such a case the surface surrounding theembedded voxel acts as an interface between that voxel and the remaininggrid system. Furthermore, the embedded voxel may comprise sub-voxelswith like or differing angular ray set distributions (see FIG. 6description). When sub-voxels or a single voxel are used in such amanner, that sub-system is considered a local voxel group, LVG asdefined above.

FIG. 3

This figure shows representative rays emanating from a surface point ona 2D plane. Each ray is traced with coupling multipliers associated withthe source surface. The appropriate integration kernel is applied foreach ray trace to accumulate surface to position multipliers in thisdepicted case.

FIG. 4

This figure illustrates that multiple rays with identical angle andsurface state values Δs and ΔΩ, may emanate from sub-surfaces followingvarious paths through a system of voxels. These pathways are combined toform single multipliers from the surface to each surrounding node,though many rays may emanate from the surface. The combination ofmultiple rays to a single multiplier greatly improves processing timewithout sacrificing accuracy. Pre-computation of sets of rays traversinga regular system of voxels can be used to improve processing speed inregular systems (See FIG. 7).

FIG. 5

This figure illustrates the bounding of a group of voxels to form alocal voxel group. In some embodiments of this 2D depiction, the innergroup of voxels is completely isolated from the outer group. In such acase, ray tracing and coupling from within the LVG terminates on thebounding surfaces, and ray tracing from outside the LVG terminates onthe boundary. This isolation provides a practical mechanism for changingout individual voxels or voxel groups to arbitrary resolution. Invarious embodiments, consistent ray set angular dependence is maintainedwithin and without a ray-set if enough memory exists to do so.Alternatively, one can map differing angular ray-set distributions fromwithin and without the LVG boundary. In further embodiments, one canutilize direct function coefficient deposition to provide a generalizedΔΩ translation mechanism (described below).

FIG. 6

This figure illustrates the data relationship of the pointer/multiplierpairs and sets of the present teachings. In the present teachings, thepointer within the pointer/multiplier table refers to either a remotevoxel interaction score terminal, a discrete surface terminal or a setof function coefficient terminals. In various embodiments, referentialhash tables can be used as illustrated in this figure to referenceparticular ray sets with pointers on a grid position basis. There arevarious ways of accomplishing this particular task, and a variety ofdata structures can be used. Depicted in this figure is the preferablelight memory footprint data structure containing ray-set indicesassociated with particular remote pointers. A benefit of theseembodiments is that one can quickly change out remote voxel interactionpoints, surfaces and functions with a minimum of processing steps whenreference is made to ray-set indices.

For example, when changing out a particular position interaction point,all pointers associated with that point are identified, and all ray setsfrom all positions passing through that point are recomputed to affectthe material property change. Use of hashing techniques as depicted,relating ray sets to terminal pointers, speeds processing of thematerial change out.

The pointer transport multiplier table of the present teachings can beestablished using a ray tracing technique from particular points withinvoxels or surfaces of voxels. Such a ray tracing technique can take twoforms, generalized with established angular sets or point to point. Itcan also be accomplished using a pre-computed representative ray-setscheme (see FIG. 7 detail).

In both the point to point and pre-computed cases, uniform distributionof points within voxels and surfaces are used in combination with anappropriate angle distribution P(Ω) are used to accumulate therepresentative ray set multipliers. Angular weights are back calculatedas discussed in the description of FIG. 7 for these examples.

For the forward ray-tracing technique, from a voxel interaction point,transport multipliers are accumulated by using a number of points withineach voxel representing the interactions within the voxel. Each point isgiven both a spatial weight, representing the volume represented by thepoint, and a position. Judicial selection of positions and weights isused to minimize mathematical operations associated with each point.

For each ray trace, the unique direction of the ray is ascertained andthe ray is further weighted by the solid angle represented by theparticular ray. In general, this is found as w_(ΔΩ)=sin(θ)ΔθΔφ/4π asdecomposed in polar, azimuthal spherical coordinate form where w_(ΔΩ)represents the discrete angular weight. The problem appropriateintegration kernel is applied representing attenuation through thesystem of voxels to further reduce the multiplier weights.

In all cases, the transport multipliers are accumulated for eachrepresentative ray or for a set of representative rays (FIG. 3) tracingthrough the system of voxels. Weighting for both discrete surfaces andsingle voxel interaction points for accumulation uses thestraightforward single collision integration kernel as accumulated oneach ray trace pathway.

In various embodiments of the present teachings, thetransport/multiplier system of FIG. 6 is generated with a hash tablereferring to the pointers for fast access. The multipliers are simplyaccumulated as a function of ray set and starting point.

The functional coefficient deposition used in various embodiments of thepresent teachings can be generated in a number of ways and has two modesof use. Each deposition to a functional coefficient is accumulatedthrough the weighting of both the straightforward single collisionintegration kernel and an appropriate functional weight for each ray-setcontributing to the coefficient. Function coefficient deposition can bepredetermined in the pre-compute option, and the weights are generallycomputed only once for any given grid system.

To accumulate a functional coefficient in various embodiments, onemethod is to compose a least squares error matrix which is the inverseof the curvature matrix (See P. R. Bevington, Data Reduction and ErrorAnalysis for the Physical Sciences, page 153, McGraw Hill Book Company,(1969) Library of Congress Catalogue number 69-16942) associated witheach ray set angle that contributes to a set of coefficients. It is wellknown in mathematics that given an orthogonal function, one can generatea coefficient matrix relating particular independent parameter samplepoints (such as indexed representative ray set direction parameters)using a least squares approach. This represents the weightingappropriate to deposit a particular sample function point to aparticular coefficient. Consider, for example, the well-known case of aspherical harmonic basis function:Y _(lm)={(2l+1)/4π(1−m)!/(1+m)!P _(lm)(cos θ)e ^(imφ)}^(1/2)Y _(l(−m))=(−1)Y _(lm*)(θ, φ)Where P_(lm) (cos θ) is an associated legendre polynomial and i in theabove polynomial represents an imaginary number. The construction of anangular function representing particle tallies as a function of discretebins is f(θ, φ)=Σ_(l)Σ_(m)C_(lm)×Y_(lm)(θ,φ) where formally thesummation of l is from 0 to ∞ and the summation of m is from −1 to +1.

As it is known before hand all possible sample point independentparameters (these are discrete ray set values of angles or directioncosines), one constructs a least squares weighting matrix by linearizingthe coefficient matrix C_(lm) to a convenient form C_(j). One thenconstructs a coefficient weight matrix over i raysets as:wij=Σ _(k)(y ^(T) y)⁻¹ y ^(T)where w_(ij) represents the weight appropriate for each ray setdirection i for j, x linearized coefficients and where

$y = {\begin{matrix}{Y_{00}1} & {Y_{00}2} & {Y_{00}3} & \ldots & {Y_{00}n} \\{Y_{1{({- 1})}}1} & {Y_{1{({- 1})}}2} & {Y_{1{({- 1})}}3} & \ldots & {Y_{00}n} \\\ldots & \; & \; & \; & \; \\{Y_{1m}1} & {Y_{1m}2} & {Y_{1m}3} & \ldots & {Y_{1m}n}\end{matrix}}$ Y₀₀i = Y₀₀(θ_(i), ϕ_(i))with each function evaluated at each i point from 1 to n. The ksummation reducing the symmetric square coefficient matrix is possiblebecause the evaluation of the function in the transport sweep is alwaysperformed at known points. The resulting weighting matrix is used tomodify transport multipliers for each ray accumulated tofunction-coefficient pointer/transport multiplier terminals C_(j). Theselinearized coefficients correspond to C_(lm) so that the function f(θ,φ) can be re-constructed with least squares fitting accuracy. Theweights, w_(ij) do not depend on actual values of ƒ(θ, φ) but rather theknown sample points θ_(i), φ_(i) associated with each particular raysetcontributing to n transport multipliers associated with the linearizedfunction coefficients. The function is fully constructed in thetransport problem sweep:C _(j)=Σ_(v) t _(v)*Σ_(i) g _(iv) *w _(ij)Where v represents each voxel contributing to a particular terminal,g_(iv) is the contribution from voxel v to the tally where thecoefficients are accumulated, and the summation over all relevant nangles was computed as part of the transport multiplier process in asetup calculation (FIG. 7 Block 2 or 2A). The summation over each vvoxel with t_(v) initial tally score at a voxel volume location is madeduring the transport sweep (FIG. 7 Block 4). Thus given a scattering orsource tally at a location, and the computed transport multipliersrepresented by Σ_(i)g_(iv)*w_(ij), the coefficients compress theoperations and explicit tally angles required from n (summation over i)explicit angular sets to smaller number of coefficients (for example,there may be 4000 angles on a side for an extreme ray effect problem,but only 36 coefficients for a P₅ surface harmonic approximation). Or itmay represent the compression from n surfaces to coefficients or otherfunctional state values of interest.

It must be remembered that while a function is being used for datacompression, it is formally a function of discrete tallies. Actualtransport multipliers still proceed from surfaces using an explicitfine-grained discrete tally ray-set structure. The function only servesto translate ray set angular systems at a surface boundary, or compressdata.

For the example case of a solid spherical harmonic function, the utilityand first mode of use is obvious. Rather than depositing to a singleinteraction tally within a voxel, appropriate only for modified P_(l)scattering the spherical harmonics function form allows for higher orderanisotropic P_(n) scattering in any given interaction voxel. It istraditional to use spherical harmonics functions of this sort for higherorder scattering computation. High order double differential scatteringdata comes in forms that are readily amenable for use with such arepresentation.

The second mode of use of these embodiments is as a data compressionscheme for surfaces. This alleviates the need for thousands ofindividual ray set accumulators on LVG surfaces to exactly represent theangular deposition distribution from voxels to surfaces. Rather thanhaving a huge number of individually tracked i ray sets accumulate at aboundary, one only needs C_(j) coefficients, which aggregate manydifferent i ray set angles. In so compressing the deposition to theC_(j) coefficients, in general fewer multiplication operations arerequired to construct an accurate surface shape and fewer memorylocations are required to store pointer multiplier pairs.

In addition to data compression, this functional form also serves topermit translation of one set of angular sets to another set at thesurface interface. An LVG surface that completely isolates an LVG orheterogeneous voxel from the general system can utilize differingangular discretization schemes. For example, one can use apoint-to-point system for the outside grid system, and a ray-tracingtechnique or pre-computed model either inside the isolated LVG. One canalso use the same system of computing discrete angles, but havediffering angular sets orders. For example, one may have hundreds of rayset angles on the outside of a system, and thousands of discrete anglesused in the inside of the isolated LVG. The functional forms permittranslation across the boundaries through functional interpolation. Inall instances, the functions are used to re-compute angular tallies overeach ray set solid angle direction. One set of functional coefficientsis used from the inside out, and another set from the outside in alongthe LVG surface boundary.

While this is one form of use of the functional coefficients, this doesnot preclude other function deposition techniques. In the methoddescribed above, any orthogonal function may be used, and specificexperiments using surface harmonics, which are related to sphericalharmonics, as well as general orthogonal polynomials have been carriedout.

Additionally, one can use B-Splines with pre-computed Bezier points toaffect data compression and translation. Wavelets might also be employedto improve data compression accuracy (See Y. Nievergelt Wavelets MadeEasy Birkhauser (1999) ISBN 0-8176-4061). Though generation ofcoefficients is different, the approach is still one of depositingtransport multipliers to a coefficient system. Such a method hasadvantages in reducing the coefficients associated with tally functionreconstruction. However the solid spherical harmonic or surface harmonicfunctional forms are preferred for data consistency, historic andtheoretical reasons. It is consistent with the forms used in doubledifferential scattering cross sections allowing for simple resolution ofscattering through consistent orthogonal functions.

FIG. 7

FIG. 7 presents a general flow diagram description of an algorithm forthe present teachings. Each process or flow block is described below.

A. Physical System Database (FIG. 7. Block 0)

Modeling of the transport of particles requires some specification ofmaterial and geometry layout associated with the transport medium. Suchphysical system input is usually obtained from a database.

B. Grid Construction (FIG. 7. Blocks 1-1A)

Consider for example a grid system of voxels. The grid overlays aphysical system being modeled with material compositions within eachvoxel. Converting a graphic image of a physical system into voxels formsthe grid system. Alternatively, specific input of a voxel grid systemcan be entered into the system (FIG. 7, 1A). Grid construction alsoconsiders variations in material composition. At the most basic level,when a single voxel encompasses multiple media, some form ofhomogenization must be applied. This may vary from a simple volumeweighted scheme to a flux-volume weighting scheme. Such methods are wellknown in the art. The present teachings can be used to generate acomplex voxel of heterogeneous sub-voxels.

For IMRT 3DRTP, the physical system is a 3D human model comprisingtissue and bone as well as any metallic insert tabs and prosthesis. Agrid system overlays the representation of the body and any materialproperties within, such as gamma ray homogenized macroscopic x-sections.This grid system may come from commercial 3D graphics packageinformation converted to a suitable grid system. Preferably, such a gridsystem is irregular forming about bone and tissue to maximize accuracyby minimizing the need to homogenize voxel material.

C. Ray Set LVG Construction—Overview (FIG. 7. Blocks 2A-5)

One can generate ray sets inline (FIG. 7, 2) or in a pre-constructedmanner (FIG. 7, 2A and 2AI). Inline ray set/LVG construction is mostappropriate for irregular grids. Pre-computed ray set geometricproperties used in LVG construction are most appropriate for fastcomputation of regular systems. Various embodiments of the presentteachings include all forms of ray set LVG construction as optionswithin the computer. Pre-computed ray set geometries are preferred foranalogue control systems.

Core to ray set modeling is the use of a single in-voxel interaction perinteraction sweep. For each ray, the collided and un-collided particledensity is used to determine the number of particles interacting, andhence subject to the Interaction Model (FIG. 7, 5), or continuing totraverse to LVG surface boundary for further particle transport (FIG. 7,4). One can generate a ray set through a stochastic process for singlecollision interaction modeling (Cashwell et al., supra). One can alsouse direct integration of particle distributions over appropriate solidangle domains to directly compute appropriate ray set geometry factors.These factors can be analytically, semi-analytically or stochasticallyderived as part of a pre-computation (FIG. 7, 2AI). They are then usedwith the appropriate discrete angular group ΔΩ frequencies associatedwith a particular representative ray, the individual length ofrepresentative ray within traversed voxels, and the appropriate singleinteraction particle transport kernel to compute LVG volume interactionand surface multipliers (FIG. 7, 2A).

Alternatively, one can utilize a technique similar to the discretetransfer method to determine ray sets passing through an LVG emanatingfrom reference voxel surface or volume (Lockwood et al., supra; Cumber,supra). The major differences between such an approach within thecontext of the present teachings is that rays emanating from volumes arenot represented solely from the centroid. Most importantly, it ispreferred to consider only the first flight interaction through the LVG,and use the separate interaction model to handle scattering. The presentteachings consider radiation transport in a forward direct approach withan iterative approach to handle particle scattering. These differencescontribute to a significant improvement in accuracy. One can use apoint-to-point method for surface-to-surface transport coupling that issimilar to DTM, however, a pure ray tracing technique with predefinedray set angular groups provides excellent results and allows forsimplified embedding of invariant voxel groups within a larger system.Furthermore, the present teachings provide for direct transmission togeneral function coefficients with the merits of the approach asdiscussed in the FIG. 6 description. Finally, the present teachingsaccumulate 1D ray set results in transport multipliers, greatlyimproving computational performance.

One can also use standard analytic direct solutions of appropriateparticle transport equations for inline (FIG. 7, 2) computation ofdiscrete particle LVG transport multipliers. One method is to utilizestochastic methods with extremely large sample sizes for large regularpolyhedron grid systems with many surfaces and overlay ray set lengthsupon LVGs. For irregular grids, one method is to establish a largenumber of points within a reference voxel volume or upon a voxel surfaceas centers of finite surfaces ΔS and sub-surfaces. The representativeray set can then be computed with a point-to-point method.

In the point to point method, the applicable angular distribution,particularly for the IM within voxel volumes and cosine for surfaces, isfactored into the associated fractions of representative rays travelingfrom the reference point to LVG surface point based on the solid anglesformed by the set of all points from the reference voxel surface orvolume.

In the forward ray-trace method, a sufficiently large number of solidangle discrete groups can be used to alleviate the need for surface P(Ω)distribution assumptions.

D. Pre-Computed Ray Set (FIG. 7. Block 2AI)

For the pre-computed ray set option, the following equations describethe processes involved for computing frequency and voxel length. Thefrequency associated with a particle ray set over a subsurface,sub-angular group is given as:

${f_{i,j}\left( {{\Delta\; S_{i}},{\Delta\;\Omega_{j}}} \right)} = \frac{\int_{\Delta\;{Si}}{\int_{\Delta\;{\Omega j}}{{p(\Omega)}{\partial\Omega}{\partial S}}}}{\int_{S}{\int_{\Omega}{{\partial\Omega}{\partial S}}}}$Where p(Ω) is the particle distribution appropriate for particlesstreaming through the applicable surface within the solid group ΔΩ_(j).Note that different levels or collision moments of p(Ω) are possible.Near particle source distributions p(Ω) are appropriate for theInteraction Model under consideration (e.g. flat voxel volumedistribution, isotropic). Relatively far from attenuated distributedsources, p(Ω) would represent a cosine distribution normal to ΔS. ΔS_(i)and ΔΩ_(j) represent ray set bounded surfaces and angular groupsappropriate for ΔR—the applicable ray set. f_(ij)(ΔS_(i),ΔΩ_(j))represents the indexed appropriate frequency of particles traversing aparticular ray set.

We now consider an average ray length within a ray set, ΔR, within voxell as:

${\Delta\;{{r_{1,}}_{i,j}\left( {{\Delta\; S_{i}},{\Delta\;\Omega_{j}}} \right)}} = \frac{\int_{\Delta\;{Si}}{\int_{\Delta\;{\Omega j}}{{r_{1}\left( {\Omega,S} \right)}{p(\Omega)}{\partial\Omega}{\partial S}}}}{\int_{\Delta\;{Si}}{\int_{\Delta\;{\Omega j}}{{p(\Omega)}{\partial\Omega}{\partial S}}}}$Where Δr_(l,i,j)(ΔS_(i), ΔΩ_(j)) is the voxel and path dependent averageray length.

The solution of these integrals, even for two-dimensional tessellationsis often non-trivial. There are frequently complex dependencies betweenΔS ΔΩ and ΔR. As such, the most general approach to solving pre-computedray set frequencies and lengths is Monte Carlo. While this reverts to astochastic process, one must remember that the computation is off-lineand does not involve attenuation—hence material properties areirrelevant. The geometric properties obtained are later combined withmaterial properties to determine transport multipliers (FIG. 10).Therefore, the results are extremely accurate as billions of particlerays can be generated without collisions and properties can be averagedto effectively solve the above integrals. The results are also genericand applicable for a particular p(Ω) with the modeled grid geometryirrespective of material.

FIG. 8

FIG. 8 presents a simple Monte Carlo algorithm for computing ray setsfor use in the present teachings as a detail of FIG. 7, Block 2AI. Ageometric local voxel group is setup (FIG. 8, B1). There are no materialproperties associated with the voxel as only geometric propertiesassociated with rays are being generated. A pre-calculation is carriedout to determine appropriate multiple representative rays associatedwith particular voxel pathway ray sets (FIG. 8, B2-B5).

One generates a source particle using the p(Ω) distribution appropriatefor the reference voxel surface or volume (FIG. 8, B2). Mostconveniently, canonical u, v and w direction cosines are generated forCartesian 3D coordinates. Use of local voxel volume source p(Ω)distributions are specific to each ray set library, and severaldistributions may be used in actual computations. Such sets representdistance moments, the first distance moment being from source (includingIM source) to LVG surface boundary. The second from LVG surface to farsurface and so on. Hence several outputs of FIG. 8 may be used in FIG. 7processing.

For IMRT 3DRTP, as well as other particle transport, various embodimentsof the instant teachings in the pre-compute mode use a two setdistribution moment approach, considering source and scattered particlesfrom voxel volumes with their particular distribution as one set. Thoseparticles emanating through an LVG surface are cosine distributed withinangular groups ΔΩ and form the second moment set.

However, as mentioned previously in the FIG. 6 description, one can usethe pre-computed set for nodes on one side of an LVG boundary, andtotally different scheme, such as ray-tracing or point-to-point ray setson the other side of a boundary. This type of an approach may bepreferable when assumptions regarding p(Ω) are inadequate.

The source particle is next projected to the LVG boundary along aparticular voxel pathway (FIG. 8, B3). The particle history is stored inaccordance with a ray set representing the pathway and limiting pathlengths are determined to appropriately develop representative rays forthe ray set (FIG. 8, B4). After a pre-calculation minimum tally isachieved (FIG. 8, B5), appropriate voxel path dependent, ray setrepresentative ray length bounds are developed (FIG. 8, B6). In a simplecase, scoring bins are established based on an even bin distributionbetween the longest and shortest ray length in the pre-calculation.Association of ray set scores and scoring bins is most effectivelyaccomplished by constructing a unique hash string associated with aparticular particle ray set. Again, ray sets thus defined may includeexplicit angular groups, ΔΩ as well as values indicating projected LVGboundaries for both the local reference system and, most importantly, asurface referenced LVG adjacent system. This latter value is used forcoupling LVGs at surfaces.

The source particle, particle projection and scoring procedures arerepeated (FIG. 8, B7-B9), this time scoring in finer detailrepresentative rays within ray sets developed in the preliminarycalculation. If additional unique ray set pathways are found, these arescored as well without multiple rays composing the set and saved forlater fine-grained ray calculations. As a practical matter, this isoften the case with many angular groups and large 3D geometries, evenwith 10⁹ pre-calculation particle histories.

Upon receipt of a tally signal or predetermined count (FIG. 8, B10),pre-computed ray set properties are saved to a database, computer fileor other storage medium (FIG. 8, B11). This particular process can bere-started for fine-grain, higher resolution calculations at a futuretime. This provides extremely high statistical accuracy for thegeometric ray set properties (FIG. 8, B12).

FIG. 9

FIG. 9 represents output for a particular ray set of the FIG. 8 processfor a flat source node emitting from a surface. This output wasgenerated using prototype code of the present invention. Line output ofFIG. 9 is described below.

Line 1: Number of Ray Sets

There can be from a few dozen to tens or even hundreds of thousands ofray sets depending upon the specification of size, angular groups andgeometry. This is the 12927^(th) ray set in a 6³, 8 angular groupsystem. The 10 represents the number of voxels traversed. [0][0][0]represents the angular group in terms of u, v and w cosine angle groups.

Lines 2-4

Line 2 reflects the hash code used to differentiate this particular rayset. The use of hash codes (or alternatively binary trees) provides anefficient mechanism to track ray sets. The direction cosines areprovided [0][0][0] followed by two entries of 336. This value representsthe unique LVG exit surface point coordinate. As it happens, this valueis also the same for the adjacent LVG exit surface coordinate when thelocal exit surface is used as a reference for an adjacent LVG system.The values that follow are pairs representing the voxel and exitingsurface index, 1 thru 6 for cubic voxels (note that one may have 24, 54or more surfaces for regular cubic voxels). Lines 3 and 4 provide theLVG surface exits for convenience.

Line 5

Line 5 represents the average path length (based on a unit 1 cubicvoxel) divided by the count of particles (not used). This is followed bythe longest and shortest ray within the ray set, and finally thefrequency of the ray set rays as sampled from p(Ω).

Lines 6-15

Lines 6-15 provide the detailed pathway, each voxel per line of theaverage representative ray in [I][j][k] coordinates followed by theemergent side, followed by the average length traversed in each voxel.

Line 16

Line 16 specifies that there are 3 representative rays for theset—similar in concept to 3 panel integration.

Line 17

Line 17 provides information for the first panel, panel 0, followed bythe average ray length to LVG boundary, maximum length and minimumlength.

Line 18

Line 18 is a continuation of the above with the relative frequency ofthe representative ray within the ray set, followed by the upper lengthinterval used.

Lines 19-28

Lines 19-28 supply the panel representative ray lengths for the pathway.

Lines 29-40 and 41-52

Lines 29-40 and 41-52 repeat the above sequence for panel 0 for theother two panels, 1 and 2 respectively. This completes the informationfor the 3 panel representative ray set.

While Monte Carlo is generally the preferred method for complexgeometries, other methods of solving the geometric integrals presentedabove for ray set frequency and voxel traversal length can be employedprovided that such methods are capable of providing data similar to thatof FIG. 9 for the pre-compute process.

FIG. 10

Pre-Compute Material Specific LVG Construction (FIG. 7. Block 2A)

Given the output presented in FIG. 9 for average ray sets and using thematerial properties provided in FIG. 7, Step 1 or 1A for the generalgrid system, the specific LVG multipliers can be constructed. FIG. 10presents a block diagram of this process.

The first step in processing a reference voxel position (FIG. 10, B1) isto allocate or reallocate a transmission accumulator (FIG. 10, B2). Theaccumulator is a function of each ray set ΔR as well as all explicitstate particles that are affected by particle-material interaction suchas energy ΔE.

The next step is to allocate terminal memory for the reference voxelposition (FIG. 10, B3). A terminal is defined as a pointer to a voxelLVG memory location either representing a discrete particle on thesurface or an interaction tally within a voxel volume, and a transportmultiplier from the reference to the terminal location. The terminalsmay also be functions of ΔR as well as other material state variables.Following this allocation, terminal discrete particle memory pointerkeyed hash tables are created for accumulating multiplier values byreferencing terminal positions of the LVG (FIG. 10, B4).

Finally, one walks through each ray set grid position system, startingfrom the reference voxel position, to compute and accumulate discreteparticle multipliers from the reference to the LVG terminals (FIG. 10,B5). The hash table aids in quickly identifying the proper bin foraccumulating the multiplier at a walked position. The transmissionaccumulator is used to tally the integration kernel transmitted valuesto each voxel. Applying the integration kernel to the ray set length,Δr_(l,i,j)(ΔS_(i),ΔΩ_(j)), and beginning with a transmission accumulatorinitially set to the fraction of particles traversing the ray,f_(i,j)(ΔS_(i),ΔΩ_(j)), one accumulates collision values for voxelvolumes for use in the IM and transmission values for LVG surfaces. Thefinal summation from the reference position to final position is thetransport multiplier.

As an example, for a gamma ray attenuation in IMRT, a transmissionmultiplier at the voxel surface would be f*Σe^(−Δr*μ), where thesummation (Σ) is carried out for each voxel on the path to the surface.The variable f is the fraction traversing the particular ray as definedabove, Δr is each voxel path length and μ is the appropriate attenuationfactor based on voxel material. There are multipliers for every materialphase space state such as energy. For surface LVG points, ray sets orangular groups may be used with unique multipliers to improve accuracy.The result for each position, ray set, angular group and material stateis a linear array of pointer-multiplier pairs where the pointerreferences an LVG particle count address in computer memory (See FIG.6). Other storage schemes for pointer and multipliers such as twosynchronized arrays are also possible. With these multipliersestablished, one only needs to sweep through the reference discreteparticle's LVG array to transport the particle to its LVG neighbor.

In some embodiments of the instant teachings, it is better from a memoryutilization standpoint to simply have a vector of multipliers that areproperly ordered to correspond to LVG terminal location sweeps. Insteadof pointer, multiplier pairs, the multipliers are arranged in a singlevector that corresponds to pointer arithmetic utilized in the sweep.This is most easily accomplished for interior LVGs that do not includeboundaries.

It is important to note that the pre-computed geometric property LVGconstruction process does not need to be carried out at every point inthe grid system. Pattern matching of material indices within the gridcan be applied to identify systems where the same multipliers may beused, and simple pointer arithmetic applied to translate the LVG arrayvalues to other identical material locations. (See J. Karkkainen & E.Ukkonen, Two-and Higher-Dimensional Pattern Matching in Optimal ExpectedTime, 29 SIAM J. COMPUT., 571-589 (1999) for examples of efficientmulti-dimensional pattern matching algorithms.) For regular grid systemswhere pre-computed LVG geometric systems are applicable, some form ofpattern matching to speed up computations is preferred, as long asmaterial compositions within the grid are not highly differentiated.Likewise, in various embodiments of the present teachings, patternmatching is preferred for use in IMRT 3DRTP when regular grids are used.

FIG. 11

A. Inline Ray Set LVG Construction (FIG. 7. Block 2)

In some embodiments, a preferred method for calculating discreteparticle transport multipliers from reference positions in irregulargrid systems throughout a single LVG is to utilize a point-to-pointmethodology somewhat similar to the ray layout of the discrete transfermethod. The goal of the inline LVG construction is to assemble a ray setbased computation of the discrete particle transport multiplierdirectly. As the spatial distribution of the source discrete particle isconstant, and the pathway to the other discrete particle space is known,any direct conventional radiation transport method can be applied tocompute the transport multiplier on a unit discrete source basis (See,e.g., Bellman et al., An Introduction to Invariant Imbedding, SIAM(1992); A. Shimizu, Development of Angular Eigenvalue Method forRadiation Transport Problems, 37 J. Nuclear Science and Technology,15-25 (2000); Olvey et al., Accuracy Comparisons for Variational R, Tand T ⁻¹ Response Matrix Formulations, 14 Annals of Nuclear Energy,203-209 (1987); Sternick et al., The Theory & Practice of IntensityModulated Radiation Therapy, Advanced Medical Publishing, 37-49 (1997)).Use of these constructions on a one-time basis necessitates the use of alarge number of angular groups to mitigate ray effects. A conventionalray tracing technique can be used in such an approach, and there aretimes when it is necessary to do so. However, it is preferable to retainthe ray set concept in preference to angular groups to link LVG surfaceseven for arbitrary or irregular grid systems. Whether using thepoint-to-point method or conventional ray tracing, the algorithm ispractically the same and is presented in FIG. 11.

The first step is to allocate memory for surfaces. For thepoint-to-point method, the angular distributions are computed associatedwith the centers of surfaces. If one is using a ray tracing technique,the angle system is predefined. In both instances, the relative solidangle area represented by a ray is used to determine initial weightingfor volume to surface coupling. Surface to surface coupling is on a perray-set basis and does not require special weighting unless functioncoefficients are used as the end points. Following block B1, ray-tracingand point-to-point methods are identical.

In FIG. 11 Block 2, one begins by tracing rays from volumes to surfacesand accumulating point multiplier pairs. A representative ray traceroutine, FIG. 11B3, is used for this process utilizing the appropriateintegration kernel. Following the coupling of volumes to surfaces, onethen couples surfaces to volumes and surfaces to surface in the steprepresented by FIG. 11 Block 4. With surfaces, each ray set can beindividually tracked to provide fine grained detail for furthertransmission, and this option is preferable when memory allows. Whenmemory is constrained, one can use function coefficients to serve assurface termination points to condense data and reduce memoryrequirements. However, accuracy will suffer as precise angularinformation is lost on the boundary.

B. Initial Discrete Particle Input (FIG. 7. Block 3)

We now consider that at voxel boundaries we may have an initialcondition of discrete particles specified with appropriate statevariables. A discrete particle count spans the entire voxel surface fromwhich it emanates. Coincidentally or alternatively, initial conditionscan also be provided as the number of source particles emanating fromvoxel volumes given as a source particle count. However, these valuescan be converted to an initial discrete particle count on the sourcevoxel surfaces. For IMRT 3DRTP, the initial conditions for modelingscattered radiation within tissue proceed from a primary direct raycalculation (FIG. 7, B9A). The interaction rate and possibly angularinformation is recorded for the first collision of the ray withinvoxels. This value is then used to construct an initial gamma raydiscrete particle count. The present teachings can also be used in totalwith a surface discrete particle count and cosine distance moment,although it is preferable to explicitly model true rays entering asystem using a representative ray approach directly. The InteractionModel of the present teachings can then be used to model scatteredradiation serving as a source for further particle transport. Functioncoefficient deposition as described in FIG. 6 can be used to handlehigher order scattering interactions.

C. Discrete Particle Transport Sweep (FIG. 7. Block 4)

Once the initial source conditions have been specified for discreteparticles, whether through Block 3 or 9A, one can proceed with thetransport sweep. During the particle sweep, particles are transported tovoxel discrete particle tallies for interaction model computation aswell as LVG surface boundaries for further transport.

This comprises simply sweeping through each discrete particle locationwith non-zero count as a reference. At each reference, one sweepsthrough the linear system of LVG terminal-multiplier pairs, applyingeach multiplier to the reference discrete particle count to accumulatefractional particle counts at the terminal discrete particle pointerlocations.

This computation can be carried out until one reaches an internalconvergence where most non-interacting particles are swept from thesystem. Variation of the internal convergence method may be needed fortransient problems where the discrete time state epoch Δt cannot beignored. Additionally, such a method might be preferable in someembodiments, depending on the computation cost associated with the IM.The transport sweep, however, may be performed for reference LVGswithout internal convergence. In this case, the IM is appliedimmediately after particles are transported to LVG boundaries. For IMRT3DRTP, it is preferable to sweep through local LVG terminals and computefurther scatters using the IM as one iterative sweep system.

FIG. 12

A. Interaction Model (FIG. 7. Block 5)

The Interaction Model receives terminal discrete particle tallies withappropriate state variables and generates new discrete particles eitheron voxel surfaces or from within the voxel itself, depending on modeltype preference. The present teachings can be used to generate anInteraction Model for a relatively large voxel in various embodiments ofthe system. This methodology is described below along with a simplecollision probability approach to creating a valid Interaction Model.Complex collision probability methods have been employed for some time.Marleau et al. provide examples related to the use of these methods inneutron calculations (Analytic Reductions for Transmission and LeakageProbabilities in Finite Tubes and Hexahedra, 104 Nucl. Sci. & Eng.,209-216 (1990); A Transport Method for Treating Three DimensionalLattices of Heterogeneous Cells, 101 Nucl. Sci. & Eng., 217-225 (1989)).These can be readily adapted for generalized particle transport inconnection with the present teachings.

Function coefficient deposition can be used as previously described tocreate spherical harmonics functions representing the angular particledistribution. These may in turn be used with high-order doubledifferential cross sections to compute detailed angular scatterinformation.

The goal of an Interaction Model is to compute the disposition ofparticles after a collision. This includes computation of collisionparameters of the primary interacting particle after collision, changesin state, as well as generation of secondary particles with new statevariables. For optics, RADAR, SONAR and radiative heat transfer,relatively simple computation of primary particle post-collisionparameters is required. For nuclear radiation processes, secondaryradiation such as recoil electrons from Compton scattering or additionalneutrons from fission processes must be generated by the InteractionModule. For IMRT 3DRTP, gamma interactions result in photoelectricabsorption and generation of photoelectrons at low γ energy. ComptonScattering with both scattered photons of reduced energy as well asrecoil secondary electrons should be modeled at intermediate γ energies.For very high γ energies above 1.02 MeV, pair production processes canalso be modeled.

For processes involving nuclear fission in critical systems, a specialmodel is required that multiplies each generational infinitemultiplication factor with the overall system Eigenvalue to determinelocal source strength. Assuming a single non-leakage parameter can beused for each voxel, this is a trivial model as is illustrated herein.

B. Simple Collision Probability Interaction Model for RadiationParticles

As mentioned above, a simple collision parameter approach can be usedfor an Interaction Model. In this approach, the ratios of macroscopiccross sections are used to determine the disposition of particlescolliding within the voxel. In order to apply such a model, there shouldbe on average less than one collision per voxel volume. For radiationtransport, ideally this criteria can be met by the limiting state meanfree path 1/Σ>d_(c), where Σ represents the least material state totalor transport macroscopic cross section and d_(c) represents the largestpossible path length across a voxel. However, practical experience hasshown that such a criteria can be significantly relaxed (See FIG. 151Aii scattering results and description).

In this simple model, a non-leakage probability is computed and appliedto all scattered and secondary particles emanating from each voxel. Thisprobability can be obtained by assuming a flat distribution andcomputing the particles that exit with integration kernel attenuation.Other methods discussed in the literature can also be used.

In order to speed convergence in this model, each successive generationof interacting particles within a voxel is subject to this samenon-leakage probability. FIG. 12 illustrates a simple flow diagram ofsuch a model. Inputs to this model include those depicted in FIG. 7,Block 4 as well as possible initial condition inputs from FIG. 7, Block9A. Grid initialization may incorporate a pre-calculation of materialparameters for voxels useful for the Interaction Model. This includesnon-leakage probability for each state (such as energy), particleabsorption fractions, state transfer fractions and transmissionfractions.

There are two approaches that can be taken with regard to non-leakageand transmission. The first approach is to assume that all scatteredparticles appear both inwardly and outwardly directed on the voxelsurface. A preferred approach is to account for further in-scatterwithin a voxel using the non-leakage probability, referred to as P_(nl).Consider a particle that scatters from state energy group g to g′ in asimple energy transfer model. We use Σr_(gg′) to represent thescatter/removal macroscopic cross section from group g to g′ and Σt_(g)to represent the total macroscopic cross section of the voxel material.A particle that scatters (and hence has been tallied as a collision) hasfor its first collision a probability of Σr_(gg′)/Σt_(g) of scatteringto group g′. However, a particle has a probability of Σr_(gg)/Σt_(g) toscatter in-group. For subsequent collisions, we have:

Σr_(gg)/Σt_(g)*p_(nl)*Σr_(gg′)/Σt_(g) subsequent in-voxel scattertransfer to g′ followed by,

Σr_(gg)/Σt_(g) ²*p_(nl) ²*Σr_(gg′)/Σt_(g) and so on for each subsequentgeneration.

It can be readily verified that as p_(nl) is less than 1.0, and alltransfer probabilities are less than 1.0, the total number of particlesin all generations that transfer from group g to g′ within such a voxelis given by:(Σr _(gg′) /Σt _(g))/(1.0−Σr _(gg) /Σt _(g) *p _(nl))

For time Eigenvalue problems, p_(nl) is multiplied by the problemEigenvalue λ. For fission problems, group dependent infinitemultiplication factor (k_(?)) represents the ratio of particles producedto particles removed. It may replace or augment scatter moments asdescribed above for sub-critical voxels. Various schemes forincorporating fission are possible.

In other embodiments, a scatter or fission generational approach top_(nl) is taken, such that different values of p_(nl) might beappropriate for different collision moments. However the 1/Σ>d_(c)collision criteria should be sufficient to limit the need to use scattertransmission moments within voxel volume Interaction Models.

Proceeding with the first inline process, a voxel position (FIG. 12, B1)is established. A given state tally (FIG. 12, B2) is then obtained.Interaction tallies may be functions of all discrete particle statevalues as previously discussed. For each state discrete particle,interaction parameters are applied. The first voxel transmission processconsiders the process of scattering given a discrete particleinteraction tally with state values (FIG. 12, B3). For P_(n) scattering,the angular group of incoming rays as scored by the interaction tally isconsidered in the voxel discrete particle response with an appropriatescatter p(Ω). Tally entrance surface ΔS_(i) may also be considered whendetermining voxel discrete particle transmission through other discretesurfaces. However, in some embodiments of the present teachings, such asthe use of a simple effective single collision voxel model withisotropic scatter, only the first interaction moment is treated in thisdetail. This is due to the complexities of multiple scatters and therelatively low statistical number of subsequent scatter.

For higher order scatter interactions, a function coefficient approachis used to determine the angular particle distribution, and this can beused to compute complex in-voxel scattering. However, such complexity isnot required for most modeling tasks; it is considered better to simplyuse smaller voxel sizes than deal with such complex operations.

One then moves from voxel transmission to final voxel volume tallies ofprocesses such as absorption, scatter and energy deposition (dose) asappropriate (FIG. 12, B4). Following this process, for all other states,the in-voxel scatter contribution to voxel volume tallies (FIG. 12, B5)is determined. The secondary transmission of in-voxel scattered discreteparticles (FIG. 12, B6) is then computed. Finally, loops through allparticle states and positions are conducted as needed to complete the IMsweep.

For 3DRTP IMRT, the above model with scatter represents a preferred,simple radiation model. However, this model can also be used in anothercalculation to determine larger voxel volume imbedding invariants asdescribed herein.

C. Other Collision Probability Interaction Model for Particles

Depending on the application, one may pre-compute voxel volumeinteractions using various methods known in the art. However, forsubsequent voxel discrete particle transmission, it is necessary toproperly disposition particles leaving the voxel in appropriate ray setstates when this is used as an explicit particle state value.

D. Generating an Interaction Model Based on the Present Teachings

Various embodiments of the present teachings can be used to generateimbedding invariants that are used for voxel volume interaction modelingso that one is not limited to small voxels, and the criteria as definedabove, namely, 1/Σ>d_(c) is met. As with LVG ray set properties, thisoperation may be performed off-line as a pre-compute, or inline aftermaterial and grid properties have been established. In either case, allthat is needed is to establish an initial boundary condition on a set ofvoxels. For this model however, all voxels combined to create a largervoxel should be within reference LVGs.

One subdivides the grid system and solves discrete particle transportacross a grid system tracking collision moment responses explicitly.Surface to volume information is retained for use in embedding the LVGinto a larger system. Several surfaces can be combined to create a largevoxel entrance surface as well as exit surfaces. Discrete particledensity is evenly distributed across these surfaces to form theappropriate integrated voxel system response to external entranceparticles. Ray tracing or point-to-point methods can be employed fromthe center of sub-surfaces to determine transport in the embeddedsystem. In some embodiments, a pre-compute method can also be used.

Void voxels can also be used for entrance and exit to allow for ray setinitial conditions and ray set based scoring. Voids serve to providedistance moment groupings of ray sets. In such a mode, this can be usedto reduce the number of explicit angular groups required for modelingwithin the LVG. On the input side, they serve to cause particlestreaming associated with far distant moment LVG ray sets. On the outputside, they are used to compute exit ray sets. Initial discrete particlegroupings for all un-collapsed state groups must be explicitly modeled(See FIG. 7, Block B9A).

For problems that involve fission, explicit system responses as afunction of collision time epoch must be utilized. The convergedinfinite system response can be determined after at least one andusually after several explicit generational responses have beendetermined. In no case may the group of voxels in a fissile system forma local critical system (Bellman, supra). Finally, data associated withthe grouped voxels is saved for use either in later calculations as partof a pre-compute, or for use in the existing calculation.

E. Convergence (FIG. 7. Block 6)

The present teachings solve particle transport as an impulsive initialvalue problem as opposed to a boundary value problem for non-fissileparticle transport. For fissile particle transport, a generationalEigenvalue can be computed based on generational fission changes. WhenEigenvalue is combined with relative reaction rate criteria, convergenceto an acceptable solution can be established.

An absolute in-system particle count relative to the initial discreteparticle input can be used to determine convergence, along with acomputation of the ratio of residual interaction tally to totalinteraction tally on a voxel volume basis. Following convergence,results should be re-normalized to reflect the residual scattered orsecondary IM particles that were not transported out of the system aspart of the sweep. This is particularly important for problems wherevoxel invariant responses are determined for incorporation into broaderproblem solutions.

F. Result Database Storage (FIG. 7. Block 7)

Results of calculations are preferably stored in a conventionalrelational or object database. Ray set data can also be stored in thismanner. This is a preferred mode of storage in the instant invention.

G. Optimization/Design Engine (FIG. 7. Block 8A)

As mentioned previously, the present invention is ideal for automateddesign and/or treatment planning optimization. Block 8A represents adesign optimization based on results of the present invention, in whichthe exact specification is outside of the invention.

For IMRT 3DRTP, the storage results of the instant invention areutilized, and simulated reasonable external rays are generated in orderto maximize the dose to target tumors while minimizing the dose tohealthy tissue. The present teachings can be used to generate surveyinitial computations, allowing for relatively fast rejection of incidentradiation that does not contribute significantly to dosing the tumor.Additionally, the present teachings are ideal for modeling scatteredradiation contribution to off-target healthy tissue. Commerciallyavailable optimization engines can be utilized to select the optimalbeam configuration and particle intensity using the computationalresults of the present teachings.

H. Initial Particle Distribution (FIG. 7. Block 9A)

As mentioned previously, the results of an optimization engine specifyan initial test particle distribution. For radiation pencil beams, thepreferred modeling procedure is to model rays directly and use theInteraction Model (FIG. 7, Block 5) to determine an initial particledistribution associated with scattered radiation. Such initial radiationbeams must have knowledge of the grid system. This may entailreconstruction of LVGs for certain specialty cases. Pencil beams may bemodeled using representative rays. For IMRT 3DRTP, a direct ray beamcalculation, using the present teachings to compute scattered radiationeffects, is preferable for direct radiation dose to target tissue.

FIG. 13

This figure shows the preliminary problem setup for a regular geometryutilizing the pre-compute option. The prototype used for this problemutilized FIG. 7 blocks 1A, 2AI, 2A, 3, 4, 6 and 7. Specific results forthis problem setup are depicted in FIG. 14. Other prototypes demonstratethe IM aspects of the present teachings in the following figures.

In the FIG. 13 preliminary problem, particles stream through the nearside shaded duct entrance in an off-cosine source distribution. Thespecific source distribution streaming through the otherwise perfectlyshielded side had a source distribution of isotropic particles uniformlydistributed over a 10×10×10 cm³ adjacent source voxel that of itself hadno attenuation. The duct system represented has a cross section 10 cmhigh and wide, and extends through the 60×60×60 cm³ system. Other sourceparticles do not stream in from boundaries, and scattering is notmodeled in order to maximize ray effect error. Boundary conditions onall sides are a perfect vacuum. A standard total particle attenuationcross section of 0.1 cm⁻¹ is used and is similar to internationalbenchmark problems, with the exception that there is no reflection ofparticles about three of the problem axes.

Selected results comparisons for this problem setup are presented inFIG. 14.

FIG. 14

Preliminary Planer Interaction Rate Results. This graphic depicts theresults of the present invention (middle values) compared to a highparticle count Monte Carlo (top values) with percent relativedifferences (low cell percentage values) for each 10³ cell. Results arepresented for a plane between 40 and 50 cm above the source plane. Onecell with 0.0 interactions represents the void duct while the shadedcell represents the maximum error for the plane. It is typical thatdirect solutions are as much as 20% to 40% off at such distances withother prior art direct methods. The signal is a few ten thousandths ofthe original source at these distances for an extreme ray-effectstreaming problem. It should also be noted that the present inventionprocesses multiple source distributions for this or other problems overa thousand times faster than Monte Carlo, making it ideal for designproblems and 3DRTP IMRT.

The Monte Carlo code used was of the inventor's construction, and whenused to compute a base case on the same computer proved to be 1000 timesslower than the present invention. FIGS. 15, 16, and 17 present theresults against a standard international reference benchmark, using theinline ray option.

FIG. 15

FIG. 15 shows current prototype results of the present invention againsta standard international benchmark problem. The prototype includes asimple interaction model, and uses variable rectangular parallelepipedvoxels. It employs an algorithm fully utilizing FIG. 7 blocks 1A, 1, 2,4, 5, and 6.

The reference problems and result comparison has been taken from “3DRadiation Transport Benchmarks for Simple Geometries with Void Region”published in a special issue of the journal Progress in Nuclear Energy,Volume 39 Number 2 ISSN 0149-1970 (2001). The specific problem modeledfrom the benchmark is problem number 1.

This problem consists of a 200×200×200 cm³ on a side cube of darkabsorbing material with a 100×100×100 cm³ central void. In the center ofthe void is a 20×20×20 cm³ source consisting of dark material. Theproblem is solved in two modes. The total macroscopic cross section forthe void region is 10⁻⁴ cm⁻¹ while the dark absorber cross section is0.1 cm⁻¹. This problem is extremely difficult for a direct method, asthe material is dark, there is little or no scatter, and the problemsize is large for the cross sections used.

The problem is solved in two modes. In the first mode, 1Ai, the problemboth regions are pure absorbers. In the second mode, problem 1Aii, bothregions have 50% scattering such that the both the absorption andscatter cross sections are 0.5×10⁻⁴ cm⁻¹ and 0.05 cm⁻¹ respectively forboth the void and dark regions. The source rate in the center block isuniformly 1 particle cm⁻³-s⁻¹. A single referential axis is provided forcomparison. The coordinate system extends from −100 cm to +100 cm foreach direction.

Compared with the present invention are respected nuclear transportcodes such as TORT, ARDRA and EVENT. Other codes such as PennTran didnot publish exact numbers, however from the graphics provided in thejournal, in all cases it appears that the present invention providessuperior results. Either the exact analytic flux was used for comparisonor the GVMP Monte Carlo code (a variant of MCNP). The Monte Carlo codewas run for 378,000 seconds to obtain the 1Aii results presented (SeeFIG. 17).

As the present invention does not compute flux directly, a small nodesize of 2×2×2cm³ was utilized to reconstruct the flux rate. This is anadditional source of error for the present teachings as the node averageflux is reported compared to the point fluxes computed by other codes.

For FIG. 15 scattering, the entire system was completely coupled in thepresent invention. The scatter problem required the modeling of fewernodes as there was an effective vacuum boundary condition about thenodal axis. The scatter problem required full modeling of all nodes inproblem 1. Node sizes were varied from the smallest 2×2×2 cm³ to20×20×20 cm³ nodes distant from the measurement axis. This wasconsistent with the methodologies used for the other codes. Ray tracingwas used for these particular results, and this required the modeling of9978 solid angles.

The present invention was run with the distinct setup and executionmodes separate. The setup was complete such that given any sourcedistribution, the execution time was a small fraction of the originaltime. FIG. 16 results depict the present invention with an LVG approachbreaking the reference benchmark problem in two. FIG. 17 providesmachine time comparisons.

FIG. 16

This figure shows the effect of an LVG surface cut in Problem 1Ai. Asthis problem has no scattering, a surface is particularly problematic tomodel. The surface selected was at 50 cm at the void/absorber interface.Three different surface results are presented. The first surface resultpresents no surface cut. The second presents 4 sub-surfaces per side (aninput to the prototype) with ray sets explicitly tracked through thesurface. The surface cut utilized a 6^(th) order surface harmonicsfunction coefficient fit per cut side. Results are shown for after thecut for the cut cases, as the results prior to the cut are identical.

With the LVG surface and only 4 sub-surfaces, adequate results wereobtained. A 6^(th) order surface harmonic function with 57 coefficientsdetermined using the techniques described in FIG. 6, provides goodagreement as well. Additional sub-surfaces can be utilized to improveresults further. The surface harmonic function was of the form:f(μ,φ)=a ₀+Σ_(m) {a _(m) P _(m)(μ)+Σ_(n) P _(mn)(μ)*[b _(m,n)*cos(nφ)+c_(m,n)*sin(nφ)]}Where the summation of m is from 1 to 6, the summation of n is from 1 tom, P_(m)(μ) represents a legendre polynomial and P_(mn)(μ) an associatedlegendre polynomial. The cosine normal to the surface is given by μ,while φ is the azimuthal angle. The coefficients, a, b and c werelinearized and fit in accordance with the methodology presented in FIG.6.FIG. 17

This figure shows the timing results comparison for FIGS. 15 and 16. Thepresent invention was run on an inexpensive PC processor. The clockspeed of the present invention machine was higher than other cases,making timing comparisons difficult. The present invention setup time isa one-time cost for any source distribution optimization problem (suchas those in 3D IMRT). As such, this computational time is used once tocouple the entire system. Following this coupling, the execution timesare presented and, even for a tightly converged scatter problem, aresignificantly faster than the setup time. Even the setup times for thepresent invention were better than those of the comparison directmethods and significantly faster than Monte Carlo.

These data, along with the FIG. 14 results, indicate a 1000 foldimprovement in speed through use of the present invention.

While the present teachings have been particularly shown and describedwith reference to various embodiments thereof, it will be understood bythose skilled in the art that various alterations in form and detail maybe made therein and various applications employed, without departingfrom the spirit and scope of the invention.

1. A non-stochastic method of linking computer memory locationsrepresenting discrete particle tallies for computing particle transport,comprising: employing massive ray tracing for characterizing invariantgeometric properties and relative particle distribution fractions in afield of voxel volumes, surfaces, and function coefficients producingpredefined ray sets; overlaying material properties associated with aproblem and traversing the fields within ray sets using ray set lengthsand particle and wave source probability distributions to determinemultipliers representing a fraction of particles transported from areference discrete particle tally to neighboring discrete particle tallylocations with a plurality of appropriate discrete variables; providingorderly specification of pointers to neighboring discrete particle tallylocations related to invariant imbedding transport multipliers from areference tally location; and sweeping through a system of referenceparticle tallies and corresponding multipliers to directly transportdiscrete particles to neighboring voxel volumes, surfaces, and functioncoefficients from an input initial condition.
 2. The method of claim 1wherein said plurality of discrete variables are used to model nuclearradiation transport.
 3. The method of claim 1 wherein said plurality ofdiscrete variables are used to model electromagnetic particle transport.4. The method of claim 3 wherein said electromagnetic particle transportcomprises infrared waves, optical waves, UV waves, or radio waves. 5.The method of claim 1 wherein said plurality of discrete variables areused to model radiative heat transfer.
 6. The method of claim 1 whereinsaid plurality of discrete variables are used to model sound waves. 7.The method of claim 1 further comprising extending neighboring discreteparticle tally multipliers from reference tallies to all discreteparticles in the system with consistent discrete values.
 8. The methodof claim 1 further comprising direct wiring of particle tallies in ananalogue computer system or an analogue digital hybrid.
 9. The method ofclaim 1 further comprising employing particle transport solutions withinspecific time epochs to model transient systems.
 10. The method of claim1 further comprising computationally producing an output indicative ofthe particle transport.
 11. The method of claim 1 further comprisingusing said plurality of discrete variables to model quantized particles.12. The method of claim 1 further comprising using said plurality ofdiscrete variables to model plasma transport.
 13. A non-stochasticmethod of linking computer memory locations representing discreteparticle tallies for computing particle transport, comprising: employingmassive tracing for characterizing invariant transport properties in afield of materials within voxel volumes with related surfaces andfunction coefficients within ray sets using ray set lengths and particleand wave source probability distributions for determining multipliersrepresenting a fraction of particles transported from a referencediscrete particle tally to neighboring discrete particle tally locationswith a plurality of appropriate discrete variables; providing orderlyspecification of pointers to neighboring discrete particle tallylocations related to invariant imbedding transport multipliers from areference tally location; and sweeping through a system of referenceparticle tallies and corresponding multipliers to directly transportdiscrete particles to neighboring voxel volumes, surfaces, and functioncoefficients from an input initial condition.
 14. A non-stochasticmethod of linking computer memory locations representing discreteparticle tallies for computing particle transport, comprising: employingmassive ray tracing for characterizing invariant geometric propertiesand relative particle distribution fractions in a field of voxelvolumes, surfaces, and function coefficients producing predefined raysets; overlaying material properties associated with a problem andtraversing the fields within ray sets using ray set lengths and particleand wave source probability distributions to determine multipliersrepresenting a fraction of particles transported from a referencediscrete particle tally to neighboring discrete particle tally locationswith a plurality of appropriate discrete variables; coalescing relativeweights between said ray sets for refining structural properties thereofand reducing multiplications; providing orderly specification ofpointers to neighboring discrete particle tally locations related toinvariant imbedding transport multipliers from a reference tallylocation; and sweeping through a system of reference particle talliesand corresponding multipliers to directly transport discrete particlesto neighboring voxel volumes, surfaces, and function coefficients froman input initial condition.
 15. The method of claim 13 or 14 furthercomprising using said plurality of discrete variables to model nuclearradiation transport.
 16. The method of claim 13 or 14 further comprisingusing said plurality of discrete variables to model electromagneticparticle transport.
 17. The method of claim 13 or 14 wherein saidelectromagnetic particle transport comprises infrared waves, opticalwaves, UV waves, or radio waves.
 18. The method of claim 13 or 14further comprising using said plurality of discrete variables to modelradiative heat transfer.
 19. The method of claim 13 or 14 furthercomprising using said plurality of discrete variables to model soundwaves.
 20. The method of claim 13 or 14 further comprising directlywiring particle tallies in an analogue computer system or an analoguedigital hybrid.
 21. The method of claim 13 or 14 further comprisingemploying particle transport solutions within specific time epochs tomodel transient systems.
 22. The method of claim 13 or 14 furthercomprising computationally producing an output indicative of theparticle transport.
 23. The method of claim 13 or 14 further comprisingusing said plurality of discrete variables to model quantized particles.24. The method of claim 13 or 14 further comprising using said pluralityof discrete variables to model plasma transport.
 25. The method of claim1 or 13 further comprising extending neighboring discrete particle tallymultipliers from reference tallies to adjacent tallies with consistentray sets to link and extend particle transport.
 26. The method of claim1 or 13 further comprising using pre-computed lengths and ray set weightfractions to link or create distant particle transport factors.
 27. Themethod of claim 1 or 14 further comprising employing numericalintegration or massive tracing and numerical integration forcharacterizing invariant geometric properties and relative particledistribution fractions in a field of voxel volumes, surfaces, andfunction coefficients producing predefined ray sets.
 28. The method ofclaim 1 or 14 further comprising employing massive tracing or numericalintegration of invariant geometric properties and relative particledistribution fractions in a field of voxel volumes, surfaces, andfunction coefficients within ray sets.
 29. The method of claim 1 or 14further comprising employing massive tracing and numerical integrationof invariant geometric properties and relative particle distributionfractions in a field of voxel volumes, surfaces, and functioncoefficients within ray sets.
 30. The method of claim 13 furthercomprising coalescing relative weights between said ray sets forrefining structural properties thereof and reducing multiplications. 31.The method of claim 13 further comprising employing numericalintegration or massive tracing and numerical integration, forcharacterizing invariant transport properties in a field of materialswithin voxel volumes with related surfaces and function coefficientswithin ray sets for determining the multipliers representing a fractionof particles transported from a reference discrete particle tally toneighboring discrete particle tally locations with a plurality ofappropriate discrete variables.
 32. A non-transitory computer readablemedium having stored thereon instructions that when executed cause oneor more processors to perform the steps of: employing massive raytracing for characterizing invariant geometric properties and relativeparticle distribution fractions in a field of voxel volumes, surfaces,and function coefficients producing predefined ray sets; overlayingmaterial properties associated with a problem and traversing the fieldswithin ray sets using ray set lengths and particle and wave sourceprobability distributions to determine multipliers representing afraction of particles transported from a reference discrete particletally to neighboring discrete particle tally locations with a pluralityof appropriate discrete variables; providing orderly specification ofpointers to neighboring discrete particle tally locations related toinvariant imbedding transport multipliers from a reference tallylocation; and sweeping through a system of reference particle talliesand corresponding multipliers to directly transport discrete particlesto neighboring voxel volumes, surfaces, and function coefficients froman input initial condition.
 33. A non-transitory computer readablemedium having stored thereon instructions that when executed cause oneor more processors to perform the steps of: employing massive tracingfor characterizing invariant transport properties in a field ofmaterials within assigning discrete particle tallies to voxel volumeswith related surfaces and function coefficients within ray sets usingray set lengths and particle and wave source probability distributionsfor determining multipliers representing a fraction of particlestransported from a reference discrete particle tally to neighboringdiscrete particle tally locations with a plurality of appropriatediscrete variables; providing orderly specification of pointers toneighboring discrete particle tally locations related to invariantimbedding transport multipliers from a reference tally location; andsweeping through a system of reference particle tallies andcorresponding multipliers to directly transport discrete particles toneighboring voxel volumes, surfaces, and function coefficients from aninput initial condition.
 34. A non-transitory computer readable mediumhaving stored thereon instructions that when executed cause one or moreprocessors to perform the steps of: employing massive ray tracing forcharacterizing invariant geometric properties and relative particledistribution fractions in a field of voxel volumes, surfaces, andfunction coefficients producing predefined ray sets; overlaying materialproperties associated with a problem and traversing the fields withinray sets using ray set lengths and particle and wave source probabilitydistributions to determine multipliers representing a fraction ofparticles transported from a reference discrete particle tally toneighboring discrete particle tally locations with a plurality ofappropriate discrete variables; coalescing relative weights between saidray sets for refining structural properties thereof and reducingmultiplications; providing orderly specification of pointers toneighboring discrete particle tally locations related to invariantimbedding transport multipliers from a reference tally location; andsweeping through a system of reference particle tallies andcorresponding multipliers to directly transport discrete particles toneighboring voxel volumes, surfaces, and function coefficients from aninput initial condition.